Proportional Reduction Of Error Statistics
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PRE proportional reduction in error lambda measures. Proportional Reduction of Error (PRE) The
The Proportionate Reduction In Error Is A Measure Of The Quizlet
concept that underlies the definition and interpretation of several measures proportionate reduction in error symbol of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the
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independent variable with errors made when making predictions that use information about the independent variable. E1 = errors of prediction made when the independent variable is ignored E2 = errors of prediction made when the prediction proportional reduction in error stata is based on the independent variable "All PRE measures are based on comparing predictive error levels that result from each of two methods of prediction" (Frankfort-Nachmias and Leon-Guerrero 2011:366). Table 12.1 on page 366 of the textbook helps us to understand this. The independent variable is number of children; the dependent variable is support for abortion. Content on this page requires a newer version of Adobe Flash Player. Two of the most commonly used PRE measures of association are lambda (λ) and gamma (γ). Two PRE Measures: Lambda and Gamma Lambda λ Appropriate for: Nominal Variables Gamma γ Appropriate for: Ordinal and Dichotomous Nominal Variables
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PRE, proportional reduction of errorPRE, proportional reduction of error having a mental medical condition financial status relatively bad relatively good total yes 390 http://www.tankonyvtar.hu/en/tartalom/tamop425/0010_2A_21_Nemeth_Renata-Simon_David_Tarsadalomstatisztika_magyar_es_angol_nyelven_eng/ch08s02.html (97,5 %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Using one of the illustrations from the previous lecture (where we considered mental health to be the independent variable and financial status to be the dependent variable), let’s guess the financial status reduction in of the individual respondents based on our knowledge of the distribution: 57% have relatively good, 43% have relatively worse financial status.Let’s imagine that the respondents turn up one by one and we have to guess their financial status as accurately as possible. What’s the best way to do that? having a mental medical condition financial status relatively bad relatively reduction in error good total yes 390 (97,5 %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Declareing each respondent to have a relatively good financial status is the safest way: thus we are wrong in 430 cases out of 1000.How does the situation change if we already know Table 1 and we can ask each respondent whether or not they have a mental medical condition?In this case we can improve the chances of our guesswork by categorizing everyone with a mental problem as having worse financial status, while those without mental problems as having better financial status. Thus the number of mistakes we make is down to 50.In other words, the guessing error characterizes the relationship of the two variables. Associational indices that work on this principle are called ’proportional reduction of error’ (PRE) indices.Calculating (λ) to get the connection of two nominal variables:8.1. egyenlet - Where:E1 is the number of categorising mistakes made without considering the independent variableE2 is the numbe